A broad literature survey of development and application of artificial neural networks in rainfall-runoff modelling

Abstract

Rainfall-Runoff (R-R) modelling is one of the most important and challenging work in the real and present world. In all-purpose, rainfall, temperature, soil moisture and infiltration are highly nonlinear and complicated parameters. These parameters have been used in R-R modelling and this modelling requires highly developed techniques and simulation for accurate forecasting. An artificial neural network (ANN) is a successful technique and it has a capability to design R-R model but selection of appropriate architecture (model) of ANN is most important challenge. To determine the significant development and application of artificial neural network in R-R modelling, a broad literature survey last 35 years (from 1979 to 2014) is done and results are presented in this survey paper. It is concluded that the architectures of ANN, such as back propagation neural network (BPN), radial basis function (RBF), and fuzzy neural network (FNN) are better evaluated over the conceptual and numerical method and worldwide recognized to be modelled the R-R.